Non-motorised public transport : the past, the present, the future
Bibliographic record
Abstract
Non-motorized public transport (NMPT) involves cycle-powered vehicles that carry several passengers and a small amount of goods; and provide flexible hail-and-ride services. Effectively they are non-motorized taxis. NMPT is widespread in developing countries, where it caters for a wide range of mobility needs. Common forms include cycle-rickshaw (Bangladesh, India), becak (Indonesia), cyclos (Vietnam, Cambodia), bicitaxi (Columbia, Cuba). Over the last 10-15 years there has also been a re-emergence of NMPT in the form of pedicabs in many developed countries because of the operating flexibility of NMPT, its eco-sustainability, and its ability to operate where use of motorized vehicles is restricted. In particular, in cities such as Berlin, London, New York and Vancouver, pedicabs are making the transition from ‘novelty’ to ‘serious’ transport mode. This is creating new transport policy/planning questions about pedicab operation and integration. This paper examines the phenomenon of NMPT and where it is heading. It uses case studies from Asia/Latin America and Europe/North America to examine emerging NMPT issues and possible responses, and how this may affect NMPT in Australia and New Zealand where it is still somewhat a ‘novelty’ but has potential as both an opportunity and a challenge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".